



"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Any           =  (DDPMScheduler,)



   def    __UpperCAmelCase    (       self    :	Dict							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      {
         '''num_train_timesteps''': 1_0_0_0,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**__lowerCamelCase				)
     return config



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,       [0.0_0_2, 0.0_2, 0.2, 2]				):
       self.check_over_configs(beta_start=__lowerCamelCase							,       beta_end=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     for variance in ["fixed_small", "fixed_large", "other"]:
       self.check_over_configs(variance_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     self.check_over_configs(thresholding=__lowerCamelCase				)
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(
             thresholding=__lowerCamelCase							,       prediction_type=__lowerCamelCase							,       sample_max_value=__lowerCamelCase							,       )



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(prediction_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     for t in [0, 5_0_0, 9_9_9]:
       self.check_over_forward(time_step=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				) - 0.0_0_9_7_9				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				) - 0.0_2				)				) < 1E-5



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      len(__lowerCamelCase				)

     _snake_case   	=      self.dummy_model()
     _snake_case   	=      self.dummy_sample_deter
     _snake_case   	=      torch.manual_seed(0				)

     for t in reversed(range(__lowerCamelCase				)				):
       # 1. predict noise residual
       _snake_case   	=      model(__lowerCamelCase							,       __lowerCamelCase				)

       # 2. predict previous mean of sample x_t-1
       _snake_case   	=      scheduler.step(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       generator=__lowerCamelCase				).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       _snake_case   	=      pred_prev_sample

     _snake_case   	=      torch.sum(torch.abs(__lowerCamelCase				)				)
     _snake_case   	=      torch.mean(torch.abs(__lowerCamelCase				)				)

     assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1E-2
     assert abs(result_mean.item() - 0.3_3_7_2				) < 1E-3



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config(prediction_type='''v_prediction'''				)
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      len(__lowerCamelCase				)

     _snake_case   	=      self.dummy_model()
     _snake_case   	=      self.dummy_sample_deter
     _snake_case   	=      torch.manual_seed(0				)

     for t in reversed(range(__lowerCamelCase				)				):
       # 1. predict noise residual
       _snake_case   	=      model(__lowerCamelCase							,       __lowerCamelCase				)

       # 2. predict previous mean of sample x_t-1
       _snake_case   	=      scheduler.step(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       generator=__lowerCamelCase				).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       _snake_case   	=      pred_prev_sample

     _snake_case   	=      torch.sum(torch.abs(__lowerCamelCase				)				)
     _snake_case   	=      torch.mean(torch.abs(__lowerCamelCase				)				)

     assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1E-2
     assert abs(result_mean.item() - 0.2_6_3_1				) < 1E-3



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 1, 0]

     scheduler.set_timesteps(timesteps=__lowerCamelCase				)

     _snake_case   	=      scheduler.timesteps

     for i, timestep in enumerate(__lowerCamelCase				):
       if i == len(__lowerCamelCase				) - 1:
         _snake_case   	=      -1
       else:
         _snake_case   	=      timesteps[i + 1]

       _snake_case   	=      scheduler.previous_timestep(__lowerCamelCase				)
       _snake_case   	=      prev_t.item()

       self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 5_1, 0]

     with self.assertRaises(__lowerCamelCase							,       msg='''`custom_timesteps` must be in descending order.'''				):
       scheduler.set_timesteps(timesteps=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [1_0_0, 8_7, 5_0, 1, 0]
     _snake_case   	=      len(__lowerCamelCase				)

     with self.assertRaises(__lowerCamelCase							,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
       scheduler.set_timesteps(num_inference_steps=__lowerCamelCase							,       timesteps=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config()
     _snake_case   	=      scheduler_class(**__lowerCamelCase				)

     _snake_case   	=      [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         __lowerCamelCase							,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,       ):
       scheduler.set_timesteps(timesteps=__lowerCamelCase				)













"""simple docstring"""
def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if divisor % 5 == 0 or divisor % 2 == 0:
              return 0
       __magic_name__      				=    1
       __magic_name__      				=    1
       while repunit:
              __magic_name__      				=    (1_0 * repunit + 1) % divisor
              repunit_index += 1
       return repunit_index


def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit - 1
       if divisor % 2 == 0:
              divisor += 1
       while least_divisible_repunit(__snake_case  ) <= limit:
              divisor += 2
       return divisor


if __name__ == "__main__":
 print(f"""{solution() = }""")